Computer Vision Tool and Technician as First Reader of Lung Cancer Screening CT

A. Ritchie, C. Sanghera, C. Jacobs, W. Zhang, J. Mayo, H. Roberts, M. Gingras, S. Pasian, L. Stewart, S. Tsai, D. Manos, J. Seely, P. Burrowes, R. Bhatia, S. Atkar-Khattra, B. van Ginneken, M. Tammemagi and S. Lam

World Conference on Lung Cancer 2015.

BACKGROUND: The recommendation by the US Preventive Services Task Force to screen high-risk smokers with low-dose computed tomography (LDCT) and the recent decision by the Centers for Medicare and Medicaid Services to fund LDCT screening under the Medicare program mean that LDCT screening will be implemented at the population level in the US and likely in other countries. With the large volume of scans that will be generated, accurate and efficient interpretation of LDCT images is key to providing a cost-effective implementation of LDCT screening to the large at risk population. OBJECTIVE: To evaluate an alternative workflow to identify and triage abnormal LDCT scans in which a technician assisted by Computer Vision (CV) software acts as first reader with the aim to reduce workload, improve speed, consistency and quality of interpretation of screening LDCT scans. METHODS: A test dataset of baseline Pan-Canadian Early Detection of Lung Cancer Study LDCT scans (New Engl J Med. 2013;369:908-17) was used. This included: 136 scans with lung cancers, 556 scans with benign nodules and 136 scans without nodules. The scans were randomly assigned for analysis by the CV software (CIRRUS Lung Screening, Diagnostic Imaging Analysis Group, Nijmegen, The Netherlands). The annotated scans were then reviewed by a technician without knowledge of the diagnosis. The scans were classified by the technician as either normal (no nodules or benign nodules only, potentially not requiring radiologist review) or abnormal (suspicious of malignancy or other abnormality requiring radiologist review). The results were compared with the Pan-Can Study radiologists. Nodules found by CIRRUS but not by the radiologist were reviewed by a subspecialty trained chest radiologist with 14 years experience in lung cancer screening (JM). RESULTS: The overall sensitivity and specificity of the technician to identify an abnormal scan were: 97.7% (95% CI: 96.3 - 98.7) and 98.0% (95% CI: 89.5 - 99.7) respectively. The technician correctly identified all the scans with malignant nodules. The time taken by the technician to read a scan was 208A,A+-120 sec. CONCLUSIONS: A technician assisted by CV software can categorize accurately abnormal scans for review by a radiologist. Pre-screening by a technician and CV software is a promising strategy for reducing workload, improving the speed, consistency and quality of scan interpretation of screening chest CTs. ACKNOWLEDGEMENTS: Supported by the Terry Fox Research Institute, The Canadian Partnership Against Cancer and the BC Cancer Foundation